/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/

// Insight classes
#include "itkLightProcessObject.h"
#include "itkTextOutput.h"

#include "itkImagePCAShapeModelEstimator.h"

// class to support progress feeback


class ShowProgressObject
{
public:
  ShowProgressObject(itk::LightProcessObject * o)
    {m_Process = o;}
  void ShowProgress()
    {std::cout << "Progress " << m_Process->GetProgress() << std::endl;}
  itk::LightProcessObject::Pointer m_Process;
};


int itkImagePCAShapeModelEstimatorTest(int, char* [] )
{
  //Data definitions
  constexpr itk::Size<2u>::SizeValueType IMGWIDTH  = 2;
  constexpr itk::Size<2u>::SizeValueType IMGHEIGHT  = 2;
  constexpr unsigned int NDIMENSION = 2;
  constexpr unsigned int NUMTRAINIMAGES = 3;
  constexpr unsigned int NUMLARGESTPC = 2;

  itk::OutputWindow::SetInstance(itk::TextOutput::New().GetPointer());

  //------------------------------------------------------
  //Create 3 simple test images with
  //------------------------------------------------------
  using InputImageType = itk::Image<double,NDIMENSION>;
  using OutputImageType = itk::Image<double,NDIMENSION>;

  using InputImageIterator = itk::ImageRegionIterator< InputImageType >;
  using OutputImageIterator = itk::ImageRegionIterator< OutputImageType >;

  InputImageType::Pointer image1 = InputImageType::New();

  InputImageType::Pointer image2 = InputImageType::New();

  InputImageType::Pointer image3 = InputImageType::New();

  InputImageType::SizeType inputImageSize = {{ IMGWIDTH, IMGHEIGHT }};

  InputImageType::IndexType index;
  index.Fill(0);
  InputImageType::RegionType region;

  region.SetSize( inputImageSize );
  region.SetIndex( index );

  //------------------------------------------------------------------------
  // Set up Image 1 first
  //------------------------------------------------------------------------

  image1->SetLargestPossibleRegion( region );
  image1->SetBufferedRegion( region );
  image1->Allocate();

  // setup the iterators
  InputImageIterator image1It( image1, image1->GetBufferedRegion() );

  //------------------------------------------------------------------------
  // Set up Image 2 first
  //------------------------------------------------------------------------

  image2->SetLargestPossibleRegion( region );
  image2->SetBufferedRegion( region );
  image2->Allocate();

  // setup the iterators
  InputImageIterator image2It( image2, image2->GetBufferedRegion() );

  //------------------------------------------------------------------------
  // Set up Image 3 first
  //------------------------------------------------------------------------

  image3->SetLargestPossibleRegion( region );
  image3->SetBufferedRegion( region );
  image3->Allocate();

  // setup the iterators
  InputImageIterator image3It( image3, image3->GetBufferedRegion() );

  //------------------------------------------------------------------------
  //Manually create and store each vector
  //------------------------------------------------------------------------
  //Image no. 1
  for( int i = 0; i< 4; i++ )
    {
    image1It.Set( 1 ); ++image1It;
    }
  //Image no. 2
  image2It.Set( 2 ); ++image2It;
  image2It.Set( 0 ); ++image2It;
  image2It.Set( 0 ); ++image2It;
  image2It.Set( 2 ); ++image2It;

  //Image no. 3
  image3It.Set( 0 ); ++image3It;
  image3It.Set( 3 ); ++image3It;
  image3It.Set( 3 ); ++image3It;
  image3It.Set( 0 ); ++image3It;

  //----------------------------------------------------------------------
  // Test code for the Shape model estimator
  //----------------------------------------------------------------------

  //----------------------------------------------------------------------
  //Set the image model estimator
  //----------------------------------------------------------------------
  using ImagePCAShapeModelEstimatorType =
      itk::ImagePCAShapeModelEstimator<InputImageType, OutputImageType>;

  ImagePCAShapeModelEstimatorType::Pointer
    applyPCAShapeEstimator = ImagePCAShapeModelEstimatorType::New();

  //----------------------------------------------------------------------
  //Set the parameters of the clusterer
  //----------------------------------------------------------------------
  applyPCAShapeEstimator->SetNumberOfTrainingImages( NUMTRAINIMAGES );
  applyPCAShapeEstimator->SetNumberOfPrincipalComponentsRequired( NUMLARGESTPC + 1 );
  applyPCAShapeEstimator->SetNumberOfPrincipalComponentsRequired( NUMLARGESTPC );
  applyPCAShapeEstimator->SetInput(0, image1);
  applyPCAShapeEstimator->SetInput(1, image2);
  applyPCAShapeEstimator->SetInput(2, image3);

  applyPCAShapeEstimator->Update();

  //Test the printself function to increase coverage
  applyPCAShapeEstimator->Print(std::cout);

  //Exercise TypeMacro in superclass
  using GenericEstimatorType = ImagePCAShapeModelEstimatorType::Superclass;
  std::cout << applyPCAShapeEstimator->GenericEstimatorType::GetNameOfClass() << std::endl;

  //Print out the number of training images and the number of principal
  //components
  std::cout << "The number of training images are: " <<
    applyPCAShapeEstimator->GetNumberOfTrainingImages() << std::endl;

  std::cout << "The number of principal components desired are: " <<
    applyPCAShapeEstimator->GetNumberOfPrincipalComponentsRequired() << std::endl;

  //Print the eigen vectors
  vnl_vector<double> eigenValues =
    applyPCAShapeEstimator->GetEigenValues();
  unsigned int numEigVal =  eigenValues.size();
  std::cout << "Number of returned eign-values: " << numEigVal << std::endl;

  std::cout << "The " <<
    applyPCAShapeEstimator->GetNumberOfPrincipalComponentsRequired() <<
    " largest eigen values are:" << std::endl;

  for(unsigned int i= 0; i< std::min( numEigVal, NUMLARGESTPC ); i++ )
    {
    std::cout << eigenValues[ i ] << std::endl;
    }
  std::cout << "" << std::endl;
  std::cout << "" << std::endl;


  //Print the MeanImage
  OutputImageType::Pointer outImage = applyPCAShapeEstimator->GetOutput( 0 );
  OutputImageIterator outImageIt( outImage, outImage->GetBufferedRegion() );
  outImageIt.GoToBegin();

  std::cout << "The mean image is:" << std::endl;
  while(!outImageIt.IsAtEnd() )
    {
    std::cout << (double)(outImageIt.Get()) << ";"  << std::endl;
    ++outImageIt;
    }
  std::cout << "  " << std::endl;

  //Print the largest two eigen vectors
  for (unsigned int j=1; j< NUMLARGESTPC + 1; j++ )
    {
    OutputImageType::Pointer outImage2 = applyPCAShapeEstimator->GetOutput( j );
    OutputImageIterator outImage2It( outImage2, outImage2->GetBufferedRegion() );
    outImage2It.GoToBegin();

    std::cout << "" << std::endl;
    std::cout << "The eigen vector number: " << j << " is:" << std::endl;
    while(!outImage2It.IsAtEnd() )
      {
      std::cout << (double) (outImage2It.Get()) << ";"  << std::endl;
      ++outImage2It;
      }
    std::cout << "  " << std::endl;

    }

  //Test for the eigen values for the test case precomputed using Matlab/Splus
  std::cout << "" << std::endl;
  if( (eigenValues[2] < 6 || eigenValues[2] > 6.1) || (eigenValues[1] >0.1) )
    {
    std::cout<< "Test Passed" << std::endl;
    }
  else
    {
    std::cout<< "Test failed" << std::endl;
    return EXIT_FAILURE;
    }


  return EXIT_SUCCESS;
}
